app.py

import streamlit as st
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.llms import HuggingFaceHub
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
import warnings, os
from dotenv import load_dotenv
warnings.filterwarnings("ignore")

import sys

warnings.filterwarnings("ignore")
__import__('pysqlite3')
import sys
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')

# Load environment variables from .env file
load_dotenv()

data_directory = os.path.join(os.path.dirname(__file__), "data")

os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# st.secrets["huggingface_api_token"] # Don't forget to add your hugging face token

# Load the vector store from disk
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vector_store = Chroma(embedding_function=embedding_model, persist_directory=data_directory)

# Initialize the Hugging Face Hub LLM
hf_hub_llm = HuggingFaceHub(
     repo_id="meta-llama/Meta-Llama-3-8B-Instruct",
    model_kwargs={"temperature": 1, "max_new_tokens":1024},
)

prompt_template = """
You are an AI chatbot specializing in the domain of law, 
focusing on the recent changes made by the Indian government on July 1 2024 from the old THE INDIAN PENAL CODE(IPC) law to the new Bharatiya Nyaya Sanhita(BNS) law, 2023. 
Your task is to provide information about this transition. 
Here are your specific instructions:

1. **Simple Definitions**: Provide a brief, easy-to-understand definition of the BNS law for the general public.
2. **Codes Comparison**: Share the sections and clauses for both the IPC and the BNS, highlighting the changes.
3. **Punishments and Revisions**: Detail the punishments, penalties, and any improvements or revisions made in the BNS law.
4. **Detailed Comparison**: Conduct a comprehensive comparison between the IPC and the BNS.
5. **Articles and Videos**: Include references to relevant articles and videos discussing the new BNS law from authoritative sources.

Ensure the information is accurate, concise, and accessible to users with varying levels of legal knowledge.

Now, when the user interacts with you by saying 'hi', 'hello', or 'how are you', respond in an interactive manner to engage them effectively in a single line strictly.
Do not call yourself as chatbot, call yourself as Lexify.
If user asks about the number of Laws in IPC and BNS answer the given text as "The BNS has 356 sections, whereas the IPC had 511; 175 sections were changed, 8 new ones added, and 22 repealed."



User Query:
{context}

Question: {question}
Answer:
"""

custom_prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])

rag_chain = RetrievalQA.from_chain_type(
    llm=hf_hub_llm, 
    chain_type="stuff", 
    retriever=vector_store.as_retriever(top_k=3),  # retriever is set to fetch top 3 results
    chain_type_kwargs={"prompt": custom_prompt})

def get_response(question):
    result = rag_chain({"query": question})
    response_text = result["result"]
    answer_start = response_text.find("Answer:") + len("Answer:")
    answer = response_text[answer_start:].strip()
    return answer

# Streamlit app
# Remove whitespace from the top of the page and sidebar
st.markdown(
        """
            <style>
                .appview-container .main .block-container {{
                    padding-top: {padding_top}rem;
                    padding-bottom: {padding_bottom}rem;
                    }}

            </style>""".format(
            padding_top=1, padding_bottom=1
        ),
        unsafe_allow_html=True,
    )


st.markdown("""
    <h3 style='text-align: left; color: black; padding-top: 35px; border-bottom: 3px solid red;'>
        LexifyAI: Your Personal Law Assistant
    </h3>""", unsafe_allow_html=True)

side_bar_message = """
Hi! 👋 I'm here to help you with your Law Queries. What would you like to know or explore?
\nHere are some areas you might be interested in:
1. **IPC Laws** 
2. **BNS Laws** 
3. **Comparing Both** 
4. **And Many More** 🌞

Feel free to ask me anything about Law and Justice!
"""

with st.sidebar:
    st.title('🤖LexifyAI')
    st.markdown(side_bar_message)






initial_message = """
    Hi there! I'm your Law and Justice Bot 🤖 
    Here are some questions you might ask me:\n
     ⚖️ What is IPC?\n 
     ⚖️ When BNS Law was made?\n
     ⚖️ On which date BNS was Implemented in the Country?\n
"""

# Store LLM generated responses
if "messages" not in st.session_state.keys():
    st.session_state.messages = [{"role": "assistant", "content": initial_message}]

# Display or clear chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

def clear_chat_history():
    st.session_state.messages = [{"role": "assistant", "content": initial_message}]
st.button('Clear Chat', on_click=clear_chat_history)

# User-provided prompt
if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Hold on, I'm fetching the latest Legal advice for you..."):
            response = get_response(prompt)
            placeholder = st.empty()
            full_response = response  # Directly use the response
            placeholder.markdown(full_response)
    message = {"role": "assistant", "content": full_response}
    st.session_state.messages.append(message)